i have never found anywhere like that. Much of this is done in Universities and now also uber labs (surprising). To me we need a place that centralises some of the thinking here. OpenAi seems to want to try and do that, but that feels more like a playground for experiments more than a discussion zone.
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Back in the 80’s my entry into this area was through one of the Usenet discussion groups so I might see if there are academic channels and mailing lists.
I think I was searching for how to evolve software and stumbled on GAs, then onto David E. Goldberg’s work, and got him to send me copies of all his papers, as well as his book. I think I’ve thrown the papers out, but recently retrieved the book from storage.
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Kalyanmoy Deb’s book is a nice companion to Goldberg’s. I don’t know why it’s not 5 out of 5 stars on amz.
https://www.amazon.com/gp/aw/d/0470743611/ref=tmm_pap_title_0?ie=UTF8&qid=1569069732&sr=8-2
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Decentralised Search Using Open Ended Evolutionary Algorithm
Sketching the architecture for a decentralised search based on an open ended evolutionary algorithm. This is just a framework right now, not much under the hood, just musings:
- foragers are individuals in a large population. They take a query, return a result (or results), and sometimes store metadata to improve future searches
- the meta-algorithm selects foragers from a large population (eg n-per page of results?), offers the results to the user, recovers feedback on usefulness of each result (eg clicked / ignored), and uses this to rate the result
- a payment to the search app from the user can be used to publish new foragers and update public forager metadata
- if results are good the algorithm may allow successful foragers to receive payment which they can use to store metadata (with a view to improving performance)
EDIT: More thoughts worth noting…
- to evolve foragers, open ended evolution works by evolving the environment as well as the solutions (foragers+search data), so I’m wondering about ways to do this. For example, the challenge (environment) starts simple and gets more complex in stages. One idea here is to mimic the evolution of search on the web: a) personal indexes (bookmarks), b) collective indexes (sharing and combining personal indexes), c) categorisation and tagging of sites in indexes, d) add sub-categories, sub-sub-categories … ontologies etc, e) … better than Google search using local user specific context and collective indexes. I imagine this can be partly manual and gradually more and more automated as foragers and indexes evolve from things that help users create indexes and categorise entries, and gradually become able to automate more of these processes. This may be a daft idea!
@jonas mentioning you in case the above is of interest. Maybe these algorithms could help with one of your projects (see the presentation posted by David above).
[BTW DrawExpress is an awesome little Android app using gestures to create diagrams of many kinds on mobile or, as above, on tablet. Exceptionally good UX.]
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@happybeing … making sense 
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@Happy Being - I thought it might help to incorporate some (mock up) components & functions/players into your diagram which could be key to i. help flesh out an ““Open Ended Evolutionary Algorithm” (OEEA) design path ii. establishing our Open AI equivalent discussion group equivalent to “centralise some of the thinking” as described by David (which I will respond to separately) and iii. create a common vision/language & safe data commons project office. It’s rough and looking at how to illustrate the components/players which would appear to need to come together for mutual benefit has been a big learning/integration exercise …. so I hope it helps and that I’m not daft as well.
In this first diagram I have connected your diagram and the r3.0 diagram under the heading “a radically democratising algorithmic driven economic model/platform” as they appear to be different views of the same meta algorithm outcome and thus complimentary.
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I’m assuming that the Meta algorithm facilitates the “ intermediation of mathematical models and algorithmic metrics as well as the design of data flow architecture?"
In this 2nd version of your architecture diagram I have inserted the aligned UBER AI preso “Multidimensional Neural networks” slide and a number of smaller diagrams numbered 1-10 which flesh out existing or add key architecture components/requirements which as you say need to be evolved e.g. indexes to underpin a decentralised search app/ultimate path
Summary of diagrams 1-10 highlighted in the previous slide
1. User - UI - This is a “ very rough” solidonsafe/”sense” decentralised search page mock up around a google help diagram … to table some concepts/ideas and picture of a possible (decentralised search, public data commons utility and user established governance system) outcome to start a discussion as to what we collectively want to create, what it looks like, the benefits, what sits behind it and the easiest (project commonssense) path to it.
2. Sharing model – This diagram illustrates a forager connecting to a sharing (public data) model described in the Open Data Institute “The role of data in AI business models” paper as the optimum AI model i.e. Mutual AGI prosumer vs proprietary AI dataplume. Nick Shadbolt makes the comment “ there ought to be some way of managing large scale data sharing efforts that are in everybody’s interest” …… which in theory the safe: data commons mutual AGI dataplume communitylink model path can enable.
3. Multidimensional Neural networks optimisation – If I understand this correctly this UBER AI Deep neural network diagram illustrates what I call as smart product matrix to define the phenotype elites/interlinks to “mutate locate replace” and which can be overlay over any community, industry product need i.e. codemap. A key initially use for a safe:commons phenotype matrix method is for safe network Data Council neural network ecosystem definition and subsequent reuse for members ecosystem i.e. Davids actors/hyperneat example.
4.Meta Algorithm – How do we earning rewards in a sharing model/OEEA? This is a link to an interesting methodology i.e.Real Time Offerings with meta utility token for multi-currency engagement and transition from NNR to SRR value exchange … we can add the time based standards approach (using the CCDM resource & need optimatisation method)
5. Deep Learning - This “Prediction Function of One Hidden layer diagram/formula” is from the Neil Lawrence Deep Learning presentation “Dimensionality & activation of the neural network … to define one of the Multi-dimensions ?) activation functions & parameters for the sharing model/OEEA formula (using the Unison language definition?s] i.e. a radically democratising algorithmic driven economic model/platform. His Guardian Data Trusts could allay our privacy fears article addresses directly the need/problem we are looking to address.
6. Safe/UIA webID sign up – This diagram illustrates a mock-up of a Holistic Code UI/UX user interface to create y/our personal via peer (in the centre) , product, project engagement definition. In the collective intelligence field they call it defining y/our learning profile within a sharing model or collective intelligence.
7. Data Council “Sensor” (solidonsafe) NODE – I am assuming each proposed Data Council Node thru the safenetwork will be an enterprise network of members in their own right to create their own codemap/neural network. The NZ Govt Health Dept has implemented a fully templated Health Data Council CCDM CCDM/data governance methodology from ward thru to hospital level data councils which provides an organisational template to build upon.
8. Rough Engagement xls & statement diagram to capture how contribution & rewards earned via the meta algorithm. To practice what we preach this meta algorithms starts with a Foundation” co sig co dev group where the project delivers to what each is promoting in a via a Collective Real Time offer to capture our generic “go explore”/OEEA method as we go.
9. Learning Academy – This diagram is a mock up overlay of the Ui Path Academy which deploys the related training programs for “actors/roles” within the Ui path Industrial AI platform/software. Our OEEA model requires learning courses in a continuous learning environment (what I call a “Living university”/ULB ) which could evolve as David described via… “the unisonweb.org lib for actors to create simulated environments in which the agents evolve and directly affect the environment in which they are tested for groups or network of actors to work together, for physical world application” using a Multidimensional Smart product matrix archive for collective training course needs definition & OEE algorithm optimisation.
10. Smart product matrix… is the overlay to deliver to a pre-defined smart AI product outcome to define the new interaction/connection gamification pts i.e. the multi-dimensional archive of phenotypic elites (each with a 0 Reserve & index meta token to distribute pre agreed margins to contributors) and so we can gradually automate some of the process.
IMHO to bring the key co-dev stakeholders together around a Open AI equivalent Foundation discussion group and safe commons/OEEA path will require a specific converging path/framework & complimentary opportunity/outcome to assess. This will need a prior discussion to align our Epic stories and address the questions … Which EPIC stories to we share? Where do our EPIC’s & stories intersect & how do they interact? What new shared EPIC’s stories do we need to establish? What adjustments do we have to make to our existing EPIC’s stories/project plan …. to frame the co-development opportunity to easily include other’s “activation functions & parameters” in key contribution areas.
This is all a bit rough so I’m hoping it helps and makes some “sense”.
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@dirvine A description of the Project commonsense approach to support your neuro evolutionary path forward which I hope is helpful.
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@dirvine To support our Open Ended Algorithm ULB/neuroevolutionary path forward …interlinking a couple of safe forum Project commonsense approach threads through the Discussion of a Safe enabled Collaborative commons/data marketplace (genetic therapy/algorithm) conversation path into an Integrated Summary & Logistical Integration Process flow…
These links need to be combined with a range of others from key project partners to create a curated set of links to which we collectively agree to contribute and progressively improve.
In the short term the Collaborate commons & data marketplace (community imarket?) discussion topic & links can be used to
i. introduce the safenetwork to non technical (& technical where appropriate) external parties for which safe is an unknown at present and
ii. progress the Co phase 0 objective of bringing the converging thinkers together (Project section 1) to this evolutionary convergence point and hopefully dominant design to open and secure our opportunity thru Project section 2. Commercial and Project Section 3 Community.
iii. Intro an initial WCCDM safe public data utility Health DApp opportunity/Project Business case leveraging off an existing application/path.
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